AI

The EU AI Act is live. Automated decision systems now carry real legal liability. Most engineering teams have no idea what their agents actually decided or why. That gap is no longer just a technical debt. It is a compliance exposure.

Claude Opus 4.8 dropped last week. Most teams are still paying frontier prices for tasks a Haiku handles in its sleep. This is a five-figure mistake happening silently across the industry right now.

When software stops working, users feel betrayed. The team feels unfairly blamed. Both reactions are understandable and both miss what is actually happening between people and the software they depend on.

The technology industry tells itself a story about quality rising to the top. The actual history of software suggests something more complicated and less comfortable.

Bad data in production ML systems almost never announces itself. It arrives quietly, passes validation, and corrupts months of decisions before anyone realises something is wrong. Here is where the lies hide and how to catch them.

Most ML failures in production are not model failures. They are data failures, pipeline failures, and monitoring failures that teams misattribute to the model because the model is the part they understand least and fear most.

Twenty years ago, teams had no idea how to run databases at scale. They made every mistake possible before the patterns solidified. We are now in the same position with GPU infrastructure, making the same mistakes, faster.

The teams shipping reliable AI products in 2026 have something in common: their infrastructure is aggressively uninteresting.

A generation of developers learned to treat the database as a dumb storage layer and move all the logic into the application. That decision is quietly running up a tab that production systems eventually pay.

Vibe coding is a genuinely useful tool for getting ideas out of your head and into a browser. It is also producing a generation of production systems that nobody fully understands. That bill is coming due.

Every serious workflow I run on AI has moved to local models. Not because cloud models are bad — because ownership, latency, and privacy compound in ways that matter more than benchmark scores.

AI is compressing the entry-level job market faster than the industry wants to admit. What that actually means for people starting out, and the only honest advice I have.

Adding an AI button to your product is not an AI strategy. Here's the difference between bolting on a model and actually re-architecting around what AI makes possible.

Everybody is racing to use the best model. The companies that will win are building the thing the model can't replace: the data, the workflow, and the distribution.

Everyone optimised their prompts. The engineers who are actually shipping reliable LLM systems moved on to a harder problem: what goes into the context window, and why.

Everyone's building agents. Almost nobody is shipping them reliably. Here's what actually goes wrong and how to design around it.

A summary of today's biggest tech news: the Musk v. Altman trial, a massive AI seed round, and market fluctuations.

A deep dive into how AI agents, multimodal models, and intent-driven development are reshaping the tech landscape in 2026.